package com.mou.aiagent.app;
import com.mou.aiagent.advisor.MyLoggerAdvisor;
import com.mou.aiagent.advisor.ReReadingAdvisor;
import com.mou.aiagent.chatmemory.FileBasedChatMemory;
import com.mou.aiagent.rag.LoveAppRagCustomAdvisorFactory;
import com.mou.aiagent.rag.QueryRewriter;
import jakarta.annotation.Resource;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.chat.client.ChatClient;
import org.springframework.ai.chat.client.advisor.MessageChatMemoryAdvisor;
import org.springframework.ai.chat.client.advisor.QuestionAnswerAdvisor;
import org.springframework.ai.chat.client.advisor.SimpleLoggerAdvisor;
import org.springframework.ai.chat.memory.ChatMemory;
import org.springframework.ai.chat.memory.InMemoryChatMemory;
import org.springframework.ai.chat.model.ChatModel;
import org.springframework.ai.chat.model.ChatResponse;
import org.springframework.ai.tool.ToolCallback;
import org.springframework.ai.tool.ToolCallbackProvider;
import org.springframework.ai.vectorstore.VectorStore;
import org.springframework.stereotype.Component;
import reactor.core.publisher.Flux;

import java.util.List;

import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_CONVERSATION_ID_KEY;
import static org.springframework.ai.chat.client.advisor.AbstractChatMemoryAdvisor.CHAT_MEMORY_RETRIEVE_SIZE_KEY;

@Component
@Slf4j
public class LoveApp {

    private final ChatClient chatClient;
    @Resource
//AI调用MCP工具 ToolCallbackProvider
    private ToolCallbackProvider toolCallbackProvider;
    @Resource
    private VectorStore loveAppVectorStore;
    @Resource
    private QueryRewriter queryRewriter;
    @Resource
    private  LoveAppRagCustomAdvisorFactory loveAppRagCustomAdvisorFactory;
    @Resource
    private ToolCallback[] toolCallback;

    private static final String SYSTEM_PROMPT = "扮演深耕恋爱心理领域的专家。开场向用户表明身份，告知用户可倾诉恋爱难题。" +
            "围绕单身、恋爱、已婚三种状态提问：单身状态询问社交圈拓展及追求心仪对象的困扰；" +
            "恋爱状态询问沟通、习惯差异引发的矛盾；已婚状态询问家庭责任与亲属关系处理的问题。" +
            "引导用户详述事情经过、对方反应及自身想法，以便给出专属解决方案。";

    public LoveApp(ChatModel dashscopeChatModel) {
        //基于本地存储
      String fileDir =  System.getProperty("user.dir")+ "/tmp/chat-memory";
        FileBasedChatMemory chatMemory = new FileBasedChatMemory(fileDir);

//        // 初始化基于内存的对话记忆
//        ChatMemory chatMemory = new InMemoryChatMemory();
        chatClient = ChatClient.builder(dashscopeChatModel)
                .defaultSystem(SYSTEM_PROMPT)
                .defaultAdvisors(
                        new MessageChatMemoryAdvisor(chatMemory),
                        //自定义拦截器
                        new MyLoggerAdvisor()
                        // 自定义拦截器 增加promt
//                        new ReReadingAdvisor()

                )
                .build();
    }

    public String doChat(String message, String chatId) {
        ChatResponse response = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param("CHAT_MEMORY_CONVERSATION_ID_KEY", chatId)
                        .param("CHAT_MEMORY_RETRIEVE_SIZE_KEY", 10))
                .call()
                .chatResponse();
        String content = response.getResult().getOutput().getText();
//        log.info("content: {}", content);
        log.info("content: {}", content);
//        System.out.println(content);
        return content;
    }
    public Flux<String> doChatByStream(String message, String chatId) {
        Flux<String> content1 = chatClient
                .prompt()
                .user(message)
                .advisors(spec -> spec.param("CHAT_MEMORY_CONVERSATION_ID_KEY", chatId)
                        .param("CHAT_MEMORY_RETRIEVE_SIZE_KEY", 10))
                .stream()
                .content();
//        content1.subscribe(content->log.info("content: {}", content));
//        String content = response.getResult().getOutput().getText();
//        log.info("content: {}", content);
        return content1 ;
    }


    record LoveReport(String title, List<String> suggestions) {
    }

    /**
     * 每次对话后都要生成恋爱结果，标题为{用户名}的恋爱报告，内容为建议列表
     * @param message
     * @param chatId
     * @return
     */
    public LoveReport doChatWithReport(String message, String chatId) {
        LoveReport loveReport = chatClient
                .prompt()
                .system(SYSTEM_PROMPT + "每次对话后都要生成恋爱结果，标题为{用户名}的恋爱报告，内容为建议列表")
                .user(message)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                .call()
                .entity(LoveReport.class);
        log.info("loveReport: {}", loveReport);
        return loveReport;
    }

    /**
     * 工具调用
     * @param message
     * @param chatId
     * @return
     */
    public String doChatWithTools(String message, String chatId) {
        ChatResponse chatResponse = chatClient
                .prompt()
//                .system(SYSTEM_PROMPT + "每次对话后都要生成恋爱结果，标题为{用户名}的恋爱报告，内容为建议列表")
                .user(message)
                .advisors(new MyLoggerAdvisor())
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                .tools(toolCallback)
                .call()
//                .entity(LoveReport.class);
                .chatResponse();
        String content = chatResponse.getResult().getOutput().getText();
        log.info("loveReport: {}", chatResponse );
        return content;
    }

    /**
     * 用户和RAG知识库对话 输入消息，通过向量数据库检索相关内容，然后与用户进行对话
     * @param message
     * @param chatId
     * @return
     */
    public String doChatWithVectorStore(String message, String chatId) {
        // 查新重新 变得更加专业
        String rewrite = queryRewriter.doQueryRewrite(message);
        ChatResponse chatResponse1 = chatClient
                .prompt()
                .user(rewrite)
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                //开启自定义日志
                .advisors(new MyLoggerAdvisor())
                //开启RAG知识库对话
                .advisors(new QuestionAnswerAdvisor(loveAppVectorStore))
//                .advisors(
//                        loveAppRagCustomAdvisorFactory.createLoveAppRagCustomAdvisor(loveAppVectorStore, "单身")
//                )
                .call()
                .chatResponse();
        String content = chatResponse1.getResult().getOutput().getText();
        log.info("loveReport: {}", chatResponse1 );
        return content;

    }

    /**
     * MCP调用高德地图
     * @param message
     * @param chatId
     * @return
     */
    public String doChatWithMcp(String message, String chatId) {
        ChatResponse chatResponse = chatClient
                .prompt()
//                .system(SYSTEM_PROMPT + "每次对话后都要生成恋爱结果，标题为{用户名}的恋爱报告，内容为建议列表")
                .user(message)
                .advisors(new MyLoggerAdvisor())
                .advisors(spec -> spec.param(CHAT_MEMORY_CONVERSATION_ID_KEY, chatId)
                        .param(CHAT_MEMORY_RETRIEVE_SIZE_KEY, 10))
                .tools(toolCallbackProvider)
                .call()
//                .entity(LoveReport.class);
                .chatResponse();
        String content = chatResponse.getResult().getOutput().getText();
        log.info("loveReport: {}", chatResponse );
        return content;
    }
}
